class_docstr: The Latent Context Embedding Additive (LCE-A) Kernel.
  
  This kernel is similar to the SACKernel, and is used when context breakdowns are
  unbserverable. It assumes the same additive structure and a spatial kernel shared
  across contexts. Rather than assuming independence, LCEAKernel models the
  correlation in the latent functions for each context through learning context
  embeddings.
code: def _set_outputscale_list(self, value: Tensor) -> None:
      if not torch.is_tensor(value):
          value = torch.as_tensor(value).to(self.raw_outputscale_list)
      self.initialize(
          raw_outputscale_list=self.raw_outputscale_list_constraint.inverse_transform(
              value
          )
      )
  
imports: from typing import Any, Dict, List, Optional
  import torch
  from gpytorch.constraints import Positive
  from gpytorch.kernels.kernel import Kernel
  from gpytorch.kernels.matern_kernel import MaternKernel
  from gpytorch.priors.torch_priors import GammaPrior
  from linear_operator.operators import DiagLinearOperator
  from linear_operator.operators.dense_linear_operator import DenseLinearOperator
  from torch import Tensor
  from torch.nn import ModuleList
label_desc: Use image node_id 5 for calling the LCEAKernel obj's underlying member method code with example usage: obj._set_outputscale_list(value) without return types